Strategic Acquisition Brief · Confidential
Financial Engines was founded on an economist’s engine that brought institutional rigor to the workplace investor. MaxiFi is the only comparable asset built since — Prof. Laurence Kotlikoff’s deterministic lifetime-planning engine that computes the provably correct plan for every household: sustainable spending, Social Security timing, Roth strategy, and the full tax code. The capability behind the thesis EFE leadership wants to prove: carrying an investor from plan participant to high-net-worth retiree under one roof.
Last year’s sale process — reported at roughly $8B — paused on weak organic growth. EFE’s response was a decisive rebuild: new CEO, a new CFO in seat as of June 2026, a new SVP of Wealth Strategy, a re-platformed technology stack, and a grant of equity to 360+ planners. The CEO has signaled publicly that EFE will re-test the M&A market once it can prove the thesis of carrying an investor from plan participant to high-net-worth retiree under one roof. An IPO has been floated as a parallel liquidity path for the sponsors.
That thesis is precisely what MaxiFi powers. The variable that paused the prior process — the workplace-to-wealth conversion rate — runs today at human-planner speed. MaxiFi lets it run at machine speed.
The first founding act was: one economist’s engine changes what the workplace investor can access. The second is: another economist’s engine changes the speed and defensibility of the conversion. The thesis EFE leadership wants to prove is already built and available. The organic-growth variable that priced the halted process is the variable MaxiFi moves.
MaxiFi is the financial-planning platform of Economic Security Planning, Inc., built over more than three decades by Professor Laurence Kotlikoff of Boston University. It uses consumption smoothing and dynamic programming to compute the single, mathematically optimal lifetime plan — solving simultaneously across Social Security strategy, Roth-conversion sequencing, withdrawal order, estate planning, and the full current tax code.
Goals-based tools answer “What is the chance you hit your number?” MaxiFi answers “What is the optimal path, and how much can I spend today without jeopardizing tomorrow?” It is not a better simulator. It is a different class of engine — deterministic, computed, and reproducible rather than sampled and probabilistic.
Prof. Laurence Kotlikoff — William Fairfield Warren Professor at Boston University; Harvard Ph.D.; former Senior Economist on the President’s Council of Economic Advisers; Fellow of the American Academy of Arts & Sciences and the Econometric Society; named by The Economist among the 25 most influential economists (2014). He intends to keep contributing to the product, help integrate, and stay on as spokesperson.
Taught by Nobel Laureate Robert Merton at MIT Sloan as an “outstanding science-based lifecycle and retirement management platform.” Merton uses MaxiFi as the reference engine in his MIT Sloan teaching. Featured in Bankrate’s “Best Financial Planning Software of 2025” roundup, cited best for near- and long-term tax planning and the decumulation phase. Economics that build on Nobel-laureate work.
30+ years of R&D in economic theory and dynamic programming — not scraped text, not a prompt-engineering layer. The kind of intellectual property a large language model cannot reconstruct by sampling tokens. Each year of refinement is time a competitor cannot buy back.
Bill Sharpe’s engine gave the workplace investor institutional rigor. Kotlikoff’s engine gives the workplace planner machine-speed, defensible lifetime answers. Two economists. Two engines. The same declared mission: make professional-grade financial planning accessible to every household, not just the wealthy.
Inside EFE, MaxiFi does one strategic thing: it converts the workplace-to-wealth flywheel from human-planner speed to machine speed. MaxiFi computes each participant’s lifetime plan from payroll and plan data. The ~370 planners inherit machine-prepared, deterministic answers instead of blank pages — “human-driven, technology-aided” made literal. Every one of the 10+ million employees with access can receive a personalized, dollar-specific lifetime plan: sustainable spending, Social Security timing, Roth strategy, estate plan. Computed correctly. At the speed of software.
That conversion rate is the organic-growth variable that priced the halted process. Adding MaxiFi is not a capability increment on top of EFE; it is the re-founding of the franchise on its own origin story — and the growth curve that changes what the franchise is worth at re-exit or IPO.
MaxiFi computes each participant’s lifetime plan from plan and payroll data; planners review, personalize, and own the relationship. Deterministic outputs with a traceable calculation — the written answer to FINRA’s model-accuracy line — so the scaled offering is exam-defensible from day one.
EFE deploys technology; it does not train models — so this is not a “train the weights” play. MaxiFi computes pre-verified lifetime plans from the plan and payroll data EFE already holds, so planners inherit machine-prepared plans instead of blank pages. Every output carries a traceable calculation — the written audit trail regulators are asking for — with no separate runtime layer to stand up, operate, or maintain.
SEC Reg Best Interest and FINRA suitability standards govern the substance of financial recommendations regardless of the interface that delivers them. Those rules are technology-neutral: the obligation follows the advice, not the medium. A firm that deploys an AI-assisted advice layer still owns the output that reaches its clients.
The 2026 exam environment has made this explicit. SEC 2026 exam priorities flag AI and technology risk: “if AI affects investor decision-making, it becomes an exam priority.” The FINRA 2026 Annual Regulatory Oversight Report dedicated a section to generative AI, naming the specific failure mode in client-facing agents:
“General-purpose AI agents may lack the necessary domain knowledge to effectively and consistently carry out complex and industry-specific tasks.”
“Complicated, multi-step agent reasoning tasks can make outcomes difficult to trace or explain, complicating auditability.”
That is a regulator describing an LLM-only financial-advice agent: wrong-prone and un-auditable. The direction is clear: AI is not a liability shield, and firms that deploy it in the advice channel own what it says. FINRA’s 2024 guidance (Reg Notice 24-09) already put firms on notice that these rules reach embedded vendor AI — “whether… developing Gen AI tools for their proprietary use or … leveraging the technology of a third party, including through embedded features in existing third-party products.” The 2026 cycle reaffirmed and deepened that posture.
For a firm scaling personalized advice to 10+ million workplace participants, the practical question is not whether to comply but how to build so that compliance is not a retrofit. MaxiFi answers it at the design level. The engine computes the plan deterministically; the output is traceable and reproducible; the answer starts from “the most a household can safely spend with what it has” — sustainable by construction — rather than the aspirational “how much will you need” that manufactures the litigable number. A confident-wrong answer at consumer scale is not a compliance risk; it is a franchise risk.
Through late 2025, AI engines widely told users that the federal estate-tax exemption would “sunset” on January 1, 2026 — reverting from roughly $13.6M to $7M per person. The One Big Beautiful Bill Act, signed July 2025, instead permanently raised it to $15M per person. A confident, plausible, entirely wrong answer, delivered at scale to households making estate plans. MaxiFi computes against the current tax code; it cannot hallucinate a statutory change that did not occur.
The most useful third-party signal is also the most recent. On May 7, 2026, CBS MoneyWatch ran an identical retirement question — a 50-year-old single woman, retiring at 65 — through Claude, ChatGPT, and Perplexity. The verdicts diverged. Kotlikoff, quoted in the piece, noted that AI engines commonly mishandle Social Security timing by averaging longevity instead of using maximum life expectancy, and may “do more harm than good.” MIT finance professor Andrew Lo was also quoted, observing that AI systems have no “best-interest duty” analogous to a human advisor’s fiduciary obligation.
“Asked whether a 50-year-old single woman could retire at 65, Claude, ChatGPT, and Perplexity gave divergent answers. Kotlikoff: AI may ‘do more harm than good,’ mishandling Social Security timing and using average rather than maximum life expectancy. MIT’s Andrew Lo: AI lacks any ‘best-interest duty’ analogous to a fiduciary.”
The divergent-verdict story →AI engines widely told users the federal estate-tax exemption would “sunset” on January 1, 2026. The One Big Beautiful Bill Act (signed July 2025) instead permanently raised the exemption to $15M per person. Dollar-specific planning decisions were made on a wrong number — delivered with confidence, by every major engine, at consumer scale.
See the Kotlikoff estate test →Neither of these is an edge case. They are the structural failure mode of a probabilistic engine giving confident answers on a domain that requires deterministic computation. The divergent-verdict story is the named, neutral proof from the national press; the estate-tax error is the dated, dollar-specific example from the public record. Both point to the same gap — and the same engine that fills it.
Over a ten-week period in 2026, Larry published a six-post sequence on his Substack, Economics Matters — 137,000+ subscribers — running named frontier models against MaxiFi on real, dollar-specific household problems. Results are dated, reproducible, and verifiable. The stakes these posts name are the exact stakes EFE faces as it scales advice to 10+ million participants.
“The AI said John and Jane can spend approximately $52,000 per year in discretionary spending. MaxiFi’s demonstrably correct answer — verifiable by inspecting its reports — is $63,382.”
Read the head-to-head →“Large language models are trained on text, not on solving optimal household financial problems. They have no internal model of taxes, Social Security, mortality risk, or lifetime budget constraints.”
Read the structural argument →“Claude understates John’s base plan’s final estate by 31 percent and his final plan’s final estate by 28 percent. On a re-prompt, Claude now says the final plan reduces John’s terminal estate by over $1 million.”
Read the estate test →“The median household leaves $182,370 of lifetime Social Security on the table. AI tells Jane a job change adds at most $35K in lifetime benefits when the right answer is $168K.”
Read the Social Security test →“I fed Claude all of John’s data. It concluded that John’s real sustainable discretionary spending was $167,000 per year — or 72.7 percent more than John can afford. If John were to spend at that level, he’d run out of money mid-retirement.”
Read the Roth test →Acquiring MaxiFi acquires the megaphone these pieces ship from — pointed, dated, and dollar-specific, at the exact audience EFE serves. Larry intends to keep contributing to the product and to stay on as spokesperson, turning a category critic into EFE’s correctness narrator. The Sharpe-to-Kotlikoff lineage is the story: the second economist’s engine, for the franchise the first one built.
MaxiFi computes the plan-participant-to-retiree lifetime plan for every employee with workplace access — conversion at machine scale, not hiring scale. The CEO’s declared objective is to prove EFE can carry an investor from plan participant to high-net-worth retiree under one roof. MaxiFi is the engine that makes that thesis run. The story is already written; the asset is available now.
The 2025 sale stalled on organic growth. Organic growth at EFE is the workplace-to-wealth conversion rate — which runs today at the speed of roughly 370 human planners. MaxiFi is the asset that moves it: machine-prepared, deterministic lifetime plans handed to planners instead of blank pages. The conversion rate changes; the franchise re-enters the market with a fundamentally different growth trajectory.
Every one of the 10+ million employees with access to EFE’s workplace ecosystem can receive a personalized, defensible, dollar-specific lifetime plan — sustainable spending, Social Security timing, Roth strategy — computed correctly, at the speed of software. “Human-driven, technology-aided, and entirely client-centered” becomes a description of an actual system, not an aspiration.
MaxiFi’s answers are computed, reproducible, and auditable. They start from “the most a household can safely spend,” sustainable by construction — not the aspirational “how much will you need” that manufactures the litigable number. In the first FINRA exam cycle to treat GenAI accuracy as a standalone topic, this is the control FINRA is asking for — not a compliance retrofit, but the design of the engine.
There is one MaxiFi. Once placed elsewhere — at a wealth platform, a fintech, or a model lab reaching for the same workplace channel — EFE’s claim to a defensible scaled-advice offering weakens permanently. Kotlikoff stays on to integrate and as spokesperson, turning the acquisition into a founding-story continuation rather than a product addition. A focused strategic process is underway.
Larry built MaxiFi over thirty years for exactly the households EFE was founded to serve. We are running a deliberately narrow process to place it where it reaches the most everyday investors — and no channel reaches more of them than EFE’s workplace.
MaxiFi is being offered through a focused strategic process. The preference is an acquisition — the engine and its founder, owned. For Edelman Financial Engines the integration path is direct: the engine inside the employee-planning platform, machine-prepared plans handed to planners, a re-rated organic-growth trajectory. Founder continuity de-risks it: Larry Kotlikoff intends to keep contributing to the product, help integrate, and stay on as spokesperson.